Overview

Dataset statistics

Number of variables12
Number of observations9800
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory918.9 KiB
Average record size in memory96.0 B

Variable types

NUM10
BOOL2

Reproduction

Analysis started2022-01-12 16:56:41.832047
Analysis finished2022-01-12 16:56:53.609255
Duration11.78 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
user is highly correlated with df_indexHigh correlation
df_index is highly correlated with userHigh correlation
Total_time_spent is highly skewed (γ1 = 42.19411122) Skewed
df_index is uniformly distributed Uniform
user is uniformly distributed Uniform
df_index has unique values Unique
user has unique values Unique
Live_sessions has 1833 (18.7%) zeros Zeros
Replay_sessions has 2980 (30.4%) zeros Zeros
Competition has 4464 (45.6%) zeros Zeros
Breakouts has 6815 (69.5%) zeros Zeros
Avg_ranking has 6876 (70.2%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE
Distinct count9800
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4994.696224489796
Minimum0
Maximum9999
Zeros1
Zeros (%)< 0.1%
Memory size76.7 KiB
2022-01-12T19:56:53.660303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile496.95
Q12497.75
median4998.5
Q37491.25
95-th percentile9497.05
Maximum9999
Range9999
Interquartile range (IQR)4993.5

Descriptive statistics

Standard deviation2886.456393
Coefficient of variation (CV)0.5779042935
Kurtosis-1.19990403
Mean4994.696224
Median Absolute Deviation (MAD)2496.5
Skewness0.000169299175
Sum48948023
Variance8331630.509
2022-01-12T19:56:53.732369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2047 1 < 0.1%
 
3371 1 < 0.1%
 
7481 1 < 0.1%
 
5432 1 < 0.1%
 
9526 1 < 0.1%
 
1330 1 < 0.1%
 
7473 1 < 0.1%
 
5424 1 < 0.1%
 
9518 1 < 0.1%
 
1322 1 < 0.1%
 
Other values (9790) 9790 99.9%
 
ValueCountFrequency (%) 
0 1 < 0.1%
 
1 1 < 0.1%
 
2 1 < 0.1%
 
3 1 < 0.1%
 
4 1 < 0.1%
 
ValueCountFrequency (%) 
9999 1 < 0.1%
 
9998 1 < 0.1%
 
9997 1 < 0.1%
 
9996 1 < 0.1%
 
9995 1 < 0.1%
 

user
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE
Distinct count9800
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4995.696224489796
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Memory size76.7 KiB
2022-01-12T19:56:53.815444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile497.95
Q12498.75
median4999.5
Q37492.25
95-th percentile9498.05
Maximum10000
Range9999
Interquartile range (IQR)4993.5

Descriptive statistics

Standard deviation2886.456393
Coefficient of variation (CV)0.5777886131
Kurtosis-1.19990403
Mean4995.696224
Median Absolute Deviation (MAD)2496.5
Skewness0.000169299175
Sum48957823
Variance8331630.509
2022-01-12T19:56:53.885508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2047 1 < 0.1%
 
7457 1 < 0.1%
 
9518 1 < 0.1%
 
3371 1 < 0.1%
 
7465 1 < 0.1%
 
5416 1 < 0.1%
 
9510 1 < 0.1%
 
3363 1 < 0.1%
 
1314 1 < 0.1%
 
5408 1 < 0.1%
 
Other values (9790) 9790 99.9%
 
ValueCountFrequency (%) 
1 1 < 0.1%
 
2 1 < 0.1%
 
3 1 < 0.1%
 
4 1 < 0.1%
 
5 1 < 0.1%
 
ValueCountFrequency (%) 
10000 1 < 0.1%
 
9999 1 < 0.1%
 
9998 1 < 0.1%
 
9997 1 < 0.1%
 
9996 1 < 0.1%
 

Grade
Real number (ℝ≥0)

Distinct count13
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.986938775510204
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size76.7 KiB
2022-01-12T19:56:53.962577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median11
Q312
95-th percentile12
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.274290662
Coefficient of variation (CV)0.2277265049
Kurtosis1.558274523
Mean9.986938776
Median Absolute Deviation (MAD)1
Skewness-1.302398315
Sum97872
Variance5.172398016
2022-01-12T19:56:54.031745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12 3236 33.0%
 
11 2044 20.9%
 
10 1254 12.8%
 
9 854 8.7%
 
8 820 8.4%
 
7 784 8.0%
 
6 263 2.7%
 
5 178 1.8%
 
4 140 1.4%
 
13 74 0.8%
 
Other values (3) 153 1.6%
 
ValueCountFrequency (%) 
1 71 0.7%
 
2 25 0.3%
 
3 57 0.6%
 
4 140 1.4%
 
5 178 1.8%
 
ValueCountFrequency (%) 
13 74 0.8%
 
12 3236 33.0%
 
11 2044 20.9%
 
10 1254 12.8%
 
9 854 8.7%
 

Active_subjects
Real number (ℝ≥0)

Distinct count18
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.206734693877551
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size76.7 KiB
2022-01-12T19:56:54.112818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum18
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.262522399
Coefficient of variation (CV)0.7055533477
Kurtosis2.236484534
Mean3.206734694
Median Absolute Deviation (MAD)1
Skewness1.360047175
Sum31426
Variance5.119007604
2022-01-12T19:56:54.183883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 2595 26.5%
 
2 2196 22.4%
 
3 1503 15.3%
 
4 1258 12.8%
 
5 715 7.3%
 
6 577 5.9%
 
7 433 4.4%
 
8 233 2.4%
 
9 134 1.4%
 
10 65 0.7%
 
Other values (8) 91 0.9%
 
ValueCountFrequency (%) 
1 2595 26.5%
 
2 2196 22.4%
 
3 1503 15.3%
 
4 1258 12.8%
 
5 715 7.3%
 
ValueCountFrequency (%) 
18 1 < 0.1%
 
17 2 < 0.1%
 
16 3 < 0.1%
 
15 3 < 0.1%
 
14 6 0.1%
 

Total_time_spent
Real number (ℝ≥0)

SKEWED
Distinct count6138
Unique (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.9221751780001
Minimum0.016666667
Maximum224290.7167
Zeros0
Zeros (%)0.0%
Memory size76.7 KiB
2022-01-12T19:56:54.265958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.016666667
5-th percentile0.5
Q15.05
median38.83333333
Q3226.6583333
95-th percentile1535.171666
Maximum224290.7167
Range224290.7
Interquartile range (IQR)221.6083333

Descriptive statistics

Standard deviation3335.934656
Coefficient of variation (CV)7.75939193
Kurtosis2395.869405
Mean429.9221752
Median Absolute Deviation (MAD)37.76666667
Skewness42.19411122
Sum4213237.317
Variance11128460.03
2022-01-12T19:56:54.332018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.25 31 0.3%
 
0.316666667 29 0.3%
 
0.35 25 0.3%
 
0.816666667 25 0.3%
 
0.233333333 25 0.3%
 
0.3 24 0.2%
 
0.4 22 0.2%
 
0.216666667 22 0.2%
 
1.033333333 21 0.2%
 
0.566666667 20 0.2%
 
Other values (6128) 9556 97.5%
 
ValueCountFrequency (%) 
0.016666667 10 0.1%
 
0.033333333 9 0.1%
 
0.05 10 0.1%
 
0.066666667 6 0.1%
 
0.083333333 11 0.1%
 
ValueCountFrequency (%) 
224290.7167 1 < 0.1%
 
123917.0667 1 < 0.1%
 
90597.83333 1 < 0.1%
 
89434.56667 1 < 0.1%
 
65437.33333 1 < 0.1%
 

Live_sessions
Real number (ℝ≥0)

ZEROS
Distinct count209
Unique (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.96561224489796
Minimum0
Maximum997
Zeros1833
Zeros (%)18.7%
Memory size76.7 KiB
2022-01-12T19:56:54.407086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile40
Maximum997
Range997
Interquartile range (IQR)7

Descriptive statistics

Standard deviation40.11378781
Coefficient of variation (CV)3.658143924
Kurtosis225.2753668
Mean10.96561224
Median Absolute Deviation (MAD)2
Skewness12.8571329
Sum107463
Variance1609.115972
2022-01-12T19:56:54.475147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 1974 20.1%
 
0 1833 18.7%
 
2 1111 11.3%
 
3 760 7.8%
 
4 552 5.6%
 
5 427 4.4%
 
6 290 3.0%
 
7 254 2.6%
 
8 229 2.3%
 
9 187 1.9%
 
Other values (199) 2183 22.3%
 
ValueCountFrequency (%) 
0 1833 18.7%
 
1 1974 20.1%
 
2 1111 11.3%
 
3 760 7.8%
 
4 552 5.6%
 
ValueCountFrequency (%) 
997 2 < 0.1%
 
958 1 < 0.1%
 
941 1 < 0.1%
 
733 1 < 0.1%
 
732 1 < 0.1%
 

Replay_sessions
Real number (ℝ≥0)

ZEROS
Distinct count81
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3148979591836736
Minimum0
Maximum380
Zeros2980
Zeros (%)30.4%
Memory size76.7 KiB
2022-01-12T19:56:54.549215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum380
Range380
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.350656569
Coefficient of variation (CV)2.519129298
Kurtosis495.6122341
Mean3.314897959
Median Absolute Deviation (MAD)1
Skewness15.35871359
Sum32486
Variance69.73346514
2022-01-12T19:56:54.618281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 2980 30.4%
 
1 2331 23.8%
 
2 1331 13.6%
 
3 819 8.4%
 
4 526 5.4%
 
5 379 3.9%
 
6 269 2.7%
 
7 184 1.9%
 
8 160 1.6%
 
9 110 1.1%
 
Other values (71) 711 7.3%
 
ValueCountFrequency (%) 
0 2980 30.4%
 
1 2331 23.8%
 
2 1331 13.6%
 
3 819 8.4%
 
4 526 5.4%
 
ValueCountFrequency (%) 
380 1 < 0.1%
 
164 1 < 0.1%
 
163 1 < 0.1%
 
144 1 < 0.1%
 
141 1 < 0.1%
 

Competition
Real number (ℝ≥0)

ZEROS
Distinct count131
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.25265306122449
Minimum0
Maximum491
Zeros4464
Zeros (%)45.6%
Memory size76.7 KiB
2022-01-12T19:56:54.701355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile16
Maximum491
Range491
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.7444596
Coefficient of variation (CV)4.172562245
Kurtosis298.0944892
Mean4.252653061
Median Absolute Deviation (MAD)1
Skewness14.67571715
Sum41676
Variance314.8658466
2022-01-12T19:56:54.769417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 4464 45.6%
 
1 1838 18.8%
 
2 865 8.8%
 
3 522 5.3%
 
4 347 3.5%
 
5 270 2.8%
 
6 193 2.0%
 
7 165 1.7%
 
8 150 1.5%
 
9 110 1.1%
 
Other values (121) 876 8.9%
 
ValueCountFrequency (%) 
0 4464 45.6%
 
1 1838 18.8%
 
2 865 8.8%
 
3 522 5.3%
 
4 347 3.5%
 
ValueCountFrequency (%) 
491 1 < 0.1%
 
489 1 < 0.1%
 
446 1 < 0.1%
 
418 1 < 0.1%
 
405 1 < 0.1%
 

Breakouts
Real number (ℝ≥0)

ZEROS
Distinct count106
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.614183673469388
Minimum0
Maximum516
Zeros6815
Zeros (%)69.5%
Memory size76.7 KiB
2022-01-12T19:56:54.847488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum516
Range516
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.68798665
Coefficient of variation (CV)5.236046261
Kurtosis514.4417667
Mean2.614183673
Median Absolute Deviation (MAD)0
Skewness18.53287383
Sum25619
Variance187.3609785
2022-01-12T19:56:54.915551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 6815 69.5%
 
1 908 9.3%
 
2 468 4.8%
 
3 300 3.1%
 
4 223 2.3%
 
5 153 1.6%
 
6 124 1.3%
 
8 92 0.9%
 
7 87 0.9%
 
10 61 0.6%
 
Other values (96) 569 5.8%
 
ValueCountFrequency (%) 
0 6815 69.5%
 
1 908 9.3%
 
2 468 4.8%
 
3 300 3.1%
 
4 223 2.3%
 
ValueCountFrequency (%) 
516 1 < 0.1%
 
499 1 < 0.1%
 
357 1 < 0.1%
 
301 1 < 0.1%
 
266 1 < 0.1%
 

Avg_ranking
Real number (ℝ≥0)

ZEROS
Distinct count1483
Unique (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4080278227341834
Minimum0.0
Maximum43.0
Zeros6876
Zeros (%)70.2%
Memory size76.7 KiB
2022-01-12T19:56:54.994623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.25
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.206991416
Coefficient of variation (CV)2.27764776
Kurtosis23.88659153
Mean1.408027823
Median Absolute Deviation (MAD)0
Skewness3.971779487
Sum13798.67266
Variance10.28479394
2022-01-12T19:56:55.064687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 6876 70.2%
 
1 578 5.9%
 
4 52 0.5%
 
5 39 0.4%
 
2 34 0.3%
 
3 28 0.3%
 
9 28 0.3%
 
7 26 0.3%
 
8 26 0.3%
 
6 25 0.3%
 
Other values (1473) 2088 21.3%
 
ValueCountFrequency (%) 
0 6876 70.2%
 
1 578 5.9%
 
1.04 1 < 0.1%
 
1.052631579 1 < 0.1%
 
1.0625 1 < 0.1%
 
ValueCountFrequency (%) 
43 1 < 0.1%
 
40.5 1 < 0.1%
 
37 1 < 0.1%
 
34.30769231 1 < 0.1%
 
33.35294118 1 < 0.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
0
9497
1
 
303
ValueCountFrequency (%) 
0 9497 96.9%
 
1 303 3.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
0
5277
1
4523
ValueCountFrequency (%) 
0 5277 53.8%
 
1 4523 46.2%
 

Interactions

2022-01-12T19:56:42.234414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:42.340511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:42.444605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:42.547702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:42.656801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.060243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.166340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.277441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.381536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.485135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.586227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.690322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.794416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:43.896509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.007630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.109066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.213161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.323262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.425354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.525333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.622421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.771557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.871648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:44.970738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.076835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.177492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.282265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.394274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.499297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.603331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.709746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.821412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:45.932020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.044671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.162221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.270944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.381951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.499452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.609477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.719131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.826229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:46.995382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.095474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.193565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.297659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.393753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.491842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.595950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.693457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.790705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.885700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:47.986702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.087748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.186702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.292707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.390705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.491706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.599714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.699713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.800690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:48.898631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.008704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.118703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.228722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.343694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.453111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.636550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.752656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.862756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:49.971855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.077951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.180045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.280138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.380229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.484324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.583163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.683791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.791889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.892981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:50.994073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.091160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.192253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.292344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.391435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.496530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.596395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.700490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.809638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:51.912844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.014977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.113989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.213030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.311327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.408416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.510509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.607713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.706704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:52.902709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:53.000715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:53.100716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-01-12T19:56:55.155771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-12T19:56:55.343954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-12T19:56:55.532126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-12T19:56:55.722444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-12T19:56:53.281790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:53.511708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexuserGradeActive_subjectsTotal_time_spentLive_sessionsReplay_sessionsCompetitionBreakoutsAvg_rankingIs_ConvertedIs_Activated
0016183.30000021023.85714301
11212113.80000001000.00000000
22312155.18333302000.00000000
3341217.21666700200.00000000
445920.25000001000.00000000
5561056.56666711100.00000000
6671213063.48333319351135.06451611
77811264.70000001000.00000001
8891211.90000001000.00000000
99101123.28333320000.00000000

Last rows

df_indexuserGradeActive_subjectsTotal_time_spentLive_sessionsReplay_sessionsCompetitionBreakoutsAvg_rankingIs_ConvertedIs_Activated
97909990999111151.36666711011.00000000
979199919992123314.483333511345.22727301
9792999299931110.13333301000.00000000
9793999399941233.91666703000.00000000
9794999499951231859.60000020121462.34482801
979599959996122106.20000003000.00000001
97969996999710468.31666730131.00000001
979799979998122175.46666706000.00000001
9798999899991111.40000002000.00000000
979999991000011329.55000002100.00000000